How To Fine-tune LLM for Arabic Instructions Using LoRA

In this article, we’ll dive deep into the process of fine-tuning a large language model (LLM) using Low-Rank Adaptation (LoRA). We’ll use a Qwen1.5–7B model on an Arabic instruction dataset.

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Let’s break down each section of the code and explore its purpose and functionality!

1. Importing Libraries:

from datasets import load_dataset
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments
from peft import LoraConfig, get_peft_model
from trl import SFTTrainer

This section imports the required libraries:
- datasets: For loading and managing datasets
- torch: The PyTorch deep learning framework
- transformers: Hugging Face’s library for working with pre-trained models
- peft: Parameter-Efficient Fine-Tuning library
- trl: TRL library for reinforcement learning and supervised fine-tuning

2. Loading the Dataset

This dataset contains six million instruction-response pairs in Arabic, which will be used to fine-tune our model.

dataset_name =…